DOCSLIB.ORG

Download [PDF, 273.71

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Download [PDF, 273.71 Republic of Albania Enterprise Surveys Data Set 1. Introduction This document provides additional information on the data collected in Albania from 13 December 2007 to 24 March 2008 as part of the Enterprise Survey, an initiative of the World Bank. The objective of the Enterprise Surveys is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Enterprise Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries. The report describes the sampling design of the survey, the structure of the dataset and additional information that may be useful when using the data, including information on non-response rates, the calculation of sample weights and country-specific factors that may have affected survey implementation. 2. Sampling Structure The whole population, or the universe, covered in the Enterprise Surveys is the non- agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. The sample for the Republic of Albania was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling1 was preferred over simple random sampling for several reasons2: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following 1 A stratified random sample is one obtained by separating the population elements into non-overlapping groups, called strata, and then selecting a simple random sample from each stratum. (Richard L. Scheaffer; Mendenhall, W.; Lyman, R., “Elementary Survey Sampling”, Fifth Edition). 2 Cochran, W., 1977, pp. 89; Lohr, Sharon, 1999, pp. 95 1 sectors: financial intermediation (group J), real estate and renting activities (group K), and all public or utilities-sectors. c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings. Three levels of stratification were used in this country: firm sector, firm size, and geographic region. The original sample design, with specific targets for these strata, is included in the attached Excel file (Sampling Report.xls.) Industry stratification was designed in the way that follows: the universe was stratified into 1 manufacturing sector (including several industries), 1 services industry -retail-, and one residual sector as defined in the sampling manual. 304 interviews were completed in total, out of an original target of 360 interviews. The main constraint to reach the target interviews was the Universe size and composition, which proved to be smaller than originally expected. Particularly, firms with more than five employees in the Services sector were scarce. Firms from sector 51 (Wholesale) were issued to compensate the shortfall in the Services sector 52 (Retail). The majority of the relevant information, including the accounting data was obtained and entered into the data base. The Productivity section had a high non-response rate on average, reaching between 20 - 25%, depending on the questionnaire. Even if call backs were done to complete the section, the response rate could not be improved by much. Size stratification was defined following the standardized definition used for the Enterprise Surveys: micro (1 to 4 employees), small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Tirana, Durres, Elbasan, Fier, Vlora were the four metropolitan areas selected in Albania. 3. Sampling implementation It was not possible to obtain a usable frame for Albania. Therefore, the design returned to first principles, using a blocks enumeration methodology. Detailed maps of major cities were obtained. These were from aerial mappings projected to a usable scale. They served as the basis of a multi-stage approach. Firstly each city (region) was divided into „blocks‟ and then the blocks were classified into strata defined by the predominant spatial use, 2 using local knowledge. The classifications used for the blocks included industrial, commercial, commercial/residential (mixed), and residential coding. The accuracy of the classification was tested using 30 pilot blocks. That test proved successful. Subsequently another 328 blocks were selected and enumerated; building by building, floor by floor. Each separate unit was identified, classified as to use and in the case of business establishments further details collected as to employee numbers, activity, name, and phone number. This enumeration of a total of 358 blocks was then employed to project to universe totals by reference to the screening results and the number of blocks in each stratum. The establishments enumerated in those blocks were then used as the frame for the selection of a sample with the aim of obtaining interviews at 360 establishments with five or more employees. In addition the World Bank requested interviews at 120 small manufacturing establishments with less than five employees, to be delivered separately as an additional survey. That target was subsequently reduced to 80 as only some 180 small manufacturing establishments had been enumerated. Disproportionate methods were used to reduce the variance of estimates. The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non- eligible units as a proportion of the total number of contacts to complete the survey was 6.8% (29 out of 425 establishments). Sample selection was carried out by the TNS team in London using the data obtained from the block enumeration. The selections for Albania were augmented by additional selections from enterprises interviewed during the BEEPS survey in 2005 and a „Large Taxpayers‟ database obtained by the local agency. To reduce non-response bias the samples was drawn in matched replicates so that each sampled establishment had at least one matched substitute (where sample available) in the event of non-contact or refusal. Local Agency team involved in the study: Local Agency Name: IDRA Research & Consulting Country: Albania Membership of international organisation: ESOMAR Activities since: 2001 Name of Project Manager Auron Pasha Name and position of other key Florian Babameto – Coordinator Adela Gjergjani – Fieldwork coordinator persons of the project: Rozeta Koci – Fieldwork coordinator Enton Coka – Data entry and quality control Enumerators involved: Enumerators: 50 enumerators in charge of the blocks enumeration and 50 interviewers in the second phase. Recruiters: the interviewers were also in charge 3 of the recruitment Other staff involved: Fieldwork Coordinators: 2 people Editing: 1 supervisor Data Entry: 4 people Sample Frame: Characteristic of sample frame used: Source: Block Enumeration Sample Frame + Albania’s Large tax payer’s data Base + BEEPS 2005 panel. Year: 2005, 2007 - 2008 Comments on the The retail sector in Albania is mainly composed by small quality of sample companies. This was first noticed when analysing the results frame: from the blocks enumeration and confirmed later during fieldwork. In addition, Manufacturing firms on the ground proved to be fewer than originally estimated prior to the beginning of the survey. From
Load more

Recommended publications
  • Lesson 3: Sampling Plan 1. Introduction to Quantitative Sampling Sampling: Definition
    Quantitative approaches Quantitative approaches Plan Lesson 3: Sampling 1. Introduction to quantitative sampling 2. Sampling error and sampling bias 3. Response rate 4. Types of "probability samples" 5. The size of the sample 6. Types of "non-probability samples" 1 2 Quantitative approaches Quantitative approaches 1. Introduction to quantitative sampling Sampling: Definition Sampling = choosing the unities (e.g. individuals, famililies, countries, texts, activities) to be investigated 3 4 Quantitative approaches Quantitative approaches Sampling: quantitative and qualitative Population and Sample "First, the term "sampling" is problematic for qualitative research, because it implies the purpose of "representing" the population sampled. Population Quantitative methods texts typically recognize only two main types of sampling: probability sampling (such as random sampling) and Sample convenience sampling." (...) any nonprobability sampling strategy is seen as "convenience sampling" and is strongly discouraged." IIIIIIIIIIIIIIII Sampling This view ignores the fact that, in qualitative research, the typical way of IIIIIIIIIIIIIIII IIIII selecting settings and individuals is neither probability sampling nor IIIII convenience sampling." IIIIIIIIIIIIIIII IIIIIIIIIIIIIIII It falls into a third category, which I will call purposeful selection; other (= «!Miniature population!») terms are purposeful sampling and criterion-based selection." IIIIIIIIIIIIIIII This is a strategy in which particular settings, persons, or activieties are selected deliberately in order to provide information that can't be gotten as well from other choices." Maxwell , Joseph A. , Qualitative research design..., 2005 , 88 5 6 Quantitative approaches Quantitative approaches Population, Sample, Sampling frame Representative sample, probability sample Population = ensemble of unities from which the sample is Representative sample = Sample that reflects the population taken in a reliable way: the sample is a «!miniature population!» Sample = part of the population that is chosen for investigation.
    [Show full text]
  • Chapter 3: Simple Random Sampling and Systematic Sampling
    Chapter 3: Simple Random Sampling and Systematic Sampling Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs that are based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade-off inherent in all sampling designs: do we select sample units at random to minimize the risk of introducing biases into the sample or do we select sample units systematically to ensure that sample units are well- distributed throughout the population? Both designs involve selecting n sample units from the N units in the population and can be implemented with or without replacement. Simple Random Sampling When the population of interest is relatively homogeneous then simple random sampling works well, which means it provides estimates that are unbiased and have high precision. When little is known about a population in advance, such as in a pilot study, simple random sampling is a common design choice. Advantages: • Easy to implement • Requires little advance knowledge about the target population Disadvantages: • Imprecise relative to other designs if the population is heterogeneous • More expensive to implement than other designs if entities are clumped and the cost to travel among units is appreciable How it is implemented: • Select n sample units at random from N available in the population All units within the population must have the same probability of being selected, therefore each and every sample of size n drawn from the population has an equal chance of being selected. There are many strategies available for selecting a random sample.
    [Show full text]
  • R(Y NONRESPONSE in SURVEY RESEARCH Proceedings of the Eighth International Workshop on Household Survey Nonresponse 24-26 September 1997
    ZUMA Zentrum für Umfragen, Melhoden und Analysen No. 4 r(y NONRESPONSE IN SURVEY RESEARCH Proceedings of the Eighth International Workshop on Household Survey Nonresponse 24-26 September 1997 Edited by Achim Koch and Rolf Porst Copyright O 1998 by ZUMA, Mannheini, Gerinany All rights reserved. No part of tliis book rnay be reproduced or utilized in any form or by aiiy means, electronic or mechanical, including photocopying, recording, or by any inforniation Storage and retrieval System, without permission in writing froni the publisher. Editors: Achim Koch and Rolf Porst Publisher: Zentrum für Umfragen, Methoden und Analysen (ZUMA) ZUMA is a member of the Gesellschaft Sozialwissenschaftlicher Infrastruktureinrichtungen e.V. (GESIS) ZUMA Board Chair: Prof. Dr. Max Kaase Dii-ector: Prof. Dr. Peter Ph. Mohlcr P.O. Box 12 21 55 D - 68072.-Mannheim Germany Phone: +49-62 1- 1246-0 Fax: +49-62 1- 1246- 100 Internet: http://www.social-science-gesis.de/ Printed by Druck & Kopie hanel, Mannheim ISBN 3-924220-15-8 Contents Preface and Acknowledgements Current Issues in Household Survey Nonresponse at Statistics Canada Larry Swin und David Dolson Assessment of Efforts to Reduce Nonresponse Bias: 1996 Survey of Income and Program Participation (SIPP) Preston. Jay Waite, Vicki J. Huggi~isund Stephen 1'. Mnck Tlie Impact of Nonresponse on the Unemployment Rate in the Current Population Survey (CPS) Ciyde Tucker arzd Brian A. Harris-Kojetin An Evaluation of Unit Nonresponse Bias in the Italian Households Budget Survey Claudio Ceccarelli, Giuliana Coccia and Fahio Crescetzzi Nonresponse in the 1996 Income Survey (Supplement to the Microcensus) Eva Huvasi anci Acfhnz Marron The Stability ol' Nonresponse Rates According to Socio-Dernographic Categories Metku Znletel anci Vasju Vehovar Understanding Household Survey Nonresponse Through Geo-demographic Coding Schemes Jolin King Response Distributions when TDE is lntroduced Hikan L.
    [Show full text]
  • Lecture 8: Sampling Methods
    Lecture 8: Sampling Methods Donglei Du ([email protected]) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) ADM 2623: Business Statistics 1 / 30 Table of contents 1 Sampling Methods Why Sampling Probability vs non-probability sampling methods Sampling with replacement vs without replacement Random Sampling Methods 2 Simple random sampling with and without replacement Simple random sampling without replacement Simple random sampling with replacement 3 Sampling error vs non-sampling error 4 Sampling distribution of sample statistic Histogram of the sample mean under SRR 5 Distribution of the sample mean under SRR: The central limit theorem Donglei Du (UNB) ADM 2623: Business Statistics 2 / 30 Layout 1 Sampling Methods Why Sampling Probability vs non-probability sampling methods Sampling with replacement vs without replacement Random Sampling Methods 2 Simple random sampling with and without replacement Simple random sampling without replacement Simple random sampling with replacement 3 Sampling error vs non-sampling error 4 Sampling distribution of sample statistic Histogram of the sample mean under SRR 5 Distribution of the sample mean under SRR: The central limit theorem Donglei Du (UNB) ADM 2623: Business Statistics 3 / 30 Why sampling? The physical impossibility of checking all items in the population, and, also, it would be too time-consuming The studying of all the items in a population would not be cost effective The sample results are usually adequate The destructive nature of certain tests Donglei Du (UNB) ADM 2623: Business Statistics 4 / 30 Sampling Methods Probability Sampling: Each data unit in the population has a known likelihood of being included in the sample.
    [Show full text]
  • Survey Design
    1 Survey Design Target population: The scope of ACES is to capture investment by all domestic, private, non-farm businesses, including agricultural non-farm business and businesses without employees. Investment made after applying for an Employer Identification Number (EIN) from the Internal Revenue Service (IRS) but before having any payroll or receipts is also included. Major exclusions are foreign operations of U.S. businesses, businesses in the U.S. territories, government operations (including the U.S. Postal Service), agricultural production companies and private households. Sampling frame: ACES collects information at the company level. The records of how the company invests are maintained at the headquarters level, and not at the location of each physical operating location. Companies may elect to have divisions within the company report, but the sampling unit and tabulation unit will be the company. A company’s importance to the survey depends on their employment, payroll, and their business activity. The greater the number of employees or the larger the payroll, the more likely, in general, a company is to be selected in the sample. The influence that the amount of payroll has on the likelihood of selection is adjusted by the business activity such that two companies with similar payroll but in different business activities will not have the same likelihood of selection. This is done to improve the quality of the estimates from any particular ACES specific industry code for business activities. Estimates are from two distinct samples from distinct frames. The first frame collects in-scope companies with employees. Companies sampled from this frame will receive an ACE-1 form, and the frame and sample are the ACE-1 frame and sample, respectively.
    [Show full text]
  • STANDARDS and GUIDELINES for STATISTICAL SURVEYS September 2006
    OFFICE OF MANAGEMENT AND BUDGET STANDARDS AND GUIDELINES FOR STATISTICAL SURVEYS September 2006 Table of Contents LIST OF STANDARDS FOR STATISTICAL SURVEYS ....................................................... i INTRODUCTION......................................................................................................................... 1 SECTION 1 DEVELOPMENT OF CONCEPTS, METHODS, AND DESIGN .................. 5 Section 1.1 Survey Planning..................................................................................................... 5 Section 1.2 Survey Design........................................................................................................ 7 Section 1.3 Survey Response Rates.......................................................................................... 8 Section 1.4 Pretesting Survey Systems..................................................................................... 9 SECTION 2 COLLECTION OF DATA................................................................................... 9 Section 2.1 Developing Sampling Frames................................................................................ 9 Section 2.2 Required Notifications to Potential Survey Respondents.................................... 10 Section 2.3 Data Collection Methodology.............................................................................. 11 SECTION 3 PROCESSING AND EDITING OF DATA...................................................... 13 Section 3.1 Data Editing ........................................................................................................
    [Show full text]
  • Unit 16: Census and Sampling
    Unit 16: Census and Sampling Summary of Video There are some questions for which an experiment can’t help us find the answer. For example, suppose we wanted to know what percentage of Americans smoke cigarettes, or what per- centage of supermarket chicken is contaminated with salmonella bacteria. There is no experi- ment that can be done to answer these types of questions. We could test every chicken on the market, or ask every person if they smoke. This is a census, a count of each and every item in a population. It seems like a census would be a straightforward way to get the most accurate, thorough information. But taking an accurate census is more difficult than you might think. The U.S. Constitution requires a census of the U.S population every ten years. In 2010, more than 308 million Americans were counted. However, the Census Bureau knows that some people are not included in this count. Undercounting certain segments of the population is a problem that can affect the representation given to a certain region as well as the federal funds it receives. What is particularly problematic is that not all groups are undercounted at the same rate. For example, the 2010 census had a hard time trying to reach renters. The first step in the U.S. Census is mailing a questionnaire to every household in the country. In 2010 about three quarters of the questionnaires were returned before the deadline. A census taker visits those households that do not respond by mail, but still not everyone is reached.
    [Show full text]
  • Second Stage Sampling for Conflict Areas: Methods and Implications Kristen Himelein, Stephanie Eckman, Siobhan Murray and Johannes Bauer 1
    Second Stage Sampling for Conflict Areas: Methods and Implications Kristen Himelein, Stephanie Eckman, Siobhan Murray and Johannes Bauer 1 Abstract: The collection of survey data from war zones or other unstable security situations is vulnerable to error because conflict often limits the options for implementation. Although there are elevated risks throughout the process, we focus here on challenges to frame construction and sample selection. We explore several alternative sampling approaches considered for the second stage selection of households for a survey in Mogadishu, Somalia. The methods are evaluated on precision, the complexity of calculations, the amount of time necessary for preparatory office work and the field implementation, and ease of implementation and verification. Unpublished manuscript prepared for the Annual Bank Conference on Africa on June 8 – 9, 2015 in Berkeley, California. Do not cite without authors’ permission. Acknowledgments: The authors would like to thank Utz Pape, from the World Bank, and Matthieu Dillais from Altai Consulting for their comments on this draft, and Hannah Mautner and Ruben Bach of IAB for their research assistance. 1 Kristen Himelein is a senior economist / statistician in the Poverty Global Practice at the World Bank. Stephanie Eckman is a senior researcher at the Institute for Employment Research (IAB) in Nuremberg, Germany. Siobhan Murray is a technical specialist in the Development Economics Research Group in the World Bank. Johannes Bauer is research fellow at the Institute for Sociology, Ludwigs-Maximilians University Munich and at the Institute for Employment Research (IAB). All views are those of the authors and do not reflect the views of their employers including the World Bank or its member countries.
    [Show full text]
  • Ch7 Sampling Techniques
    7 - 1 Chapter 7. Sampling Techniques Introduction to Sampling Distinguishing Between a Sample and a Population Simple Random Sampling Step 1. Defining the Population Step 2. Constructing a List Step 3. Drawing the Sample Step 4. Contacting Members of the Sample Stratified Random Sampling Convenience Sampling Quota Sampling Thinking Critically About Everyday Information Sample Size Sampling Error Evaluating Information From Samples Case Analysis General Summary Detailed Summary Key Terms Review Questions/Exercises 7 - 2 Introduction to Sampling The way in which we select a sample of individuals to be research participants is critical. How we select participants (random sampling) will determine the population to which we may generalize our research findings. The procedure that we use for assigning participants to different treatment conditions (random assignment) will determine whether bias exists in our treatment groups (Are the groups equal on all known and unknown factors?). We address random sampling in this chapter; we will address random assignment later in the book. If we do a poor job at the sampling stage of the research process, the integrity of the entire project is at risk. If we are interested in the effect of TV violence on children, which children are we going to observe? Where do they come from? How many? How will they be selected? These are important questions. Each of the sampling techniques described in this chapter has advantages and disadvantages. Distinguishing Between a Sample and a Population Before describing sampling procedures, we need to define a few key terms. The term population means all members that meet a set of specifications or a specified criterion.
    [Show full text]
  • METHOD GUIDE 3 Survey Sampling and Administration
    METHOD GUIDE 3 Survey sampling and administration Alexandre Barbosa, Marcelo Pitta, Fabio Senne and Maria Eugênia Sózio Regional Center for Studies on the Development of the Information Society (Cetic.br), Brazil November 2016 1 Table of Contents Global Kids Online ......................................................................................................................... 3 Abstract .......................................................................................................................................... 4 Key issues ...................................................................................................................................... 5 Main approaches and identifying good practice ......................................................................... 7 Survey frame and sources of information .................................................................................................................... 8 Methods of data collection ................................................................................................................................................ 8 Choosing an appropriate method of data collection.......................................................................................................... 9 Sampling plan.................................................................................................................................................................. 11 Target population .......................................................................................................................................................
    [Show full text]
  • Sample Surveys Test Review SOLUTIONS/EXPLANATIONS – Multiple Choice Questions
    Sample Surveys Test Review SOLUTIONS/EXPLANATIONS – Multiple Choice questions Correct answers are bolded. Explanations are in red. 1. Ann Landers, who wrote a daily advice column appearing in newspapers across the country, once asked her readers, “If you had to do it all over again, would you have children?” Of the more than 10,000 readers who responded, 70% said no. What does this show? (A) The survey is meaningless because of voluntary response bias. Explanation: Voluntary response bias is powerful – it means only people with strong opinions about the topic will respond. It will always give meaningless information for that reason, no matter what the “characteristics of her readers” include. (B) No meaningful conclusion is possible without knowing something more about the characteristics of her readers. (See above) (C) The survey would have been more meaningful if she had picked a random sample of the 10,000 readers who responded. This is nonsense. If she picks a random sample of the 10,000 people, these are the people who voluntarily responded. It then becomes a biased random sample and is still meaningless. (D) The survey would have been more meaningful if she had used a control group. Also nonsense. How do you have a control group with a voluntary survey? “Hey, you, please DON’T take this survey.” (E) This was a legitimate sample, randomly drawn from her readers and of sufficient size to allow the conclusion that most of her readers who are parents would have second thoughts about having children. NOT random, hello. 2. Which of the following are true statements? I.
    [Show full text]
  • Interactive Lecture Notes 03-Sampling, Surveys and Gathering Useful Data
    Author: Brenda Gunderson, Ph.D., 2015 License: Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution- NonCommercial-Share Alike 3.0 Unported License: http://creativecommons.org/licenses/by-nc-sa/3.0/ The University of Michigan Open.Michigan initiative has reviewed this material in accordance with U.S. Copyright Law and have tried to maximize your ability to use, share, and adapt it. The attribution key provides information about how you may share and adapt this material. Copyright holders of content included in this material should contact [email protected] with any questions, corrections, or clarification regarding the use of content. For more information about how to attribute these materials visit: http://open.umich.edu/education/about/terms-of-use. Some materials are used with permission from the copyright holders. You may need to obtain new permission to use those materials for other uses. This includes all content from: Attribution Key For more information see: http:://open.umich.edu/wiki/AttributionPolicy Content the copyright holder, author, or law permits you to use, share and adapt: Creative Commons Attribution-NonCommercial-Share Alike License Public Domain – Self Dedicated: Works that a copyright holder has dedicated to the public domain. Make Your Own Assessment Content Open.Michigan believes can be used, shared, and adapted because it is ineligible for copyright. Public Domain – Ineligible. Works that are ineligible for copyright protection in the U.S. (17 USC §102(b)) *laws in your jurisdiction may differ. Content Open.Michigan has used under a Fair Use determination Fair Use: Use of works that is determined to be Fair consistent with the U.S.
    [Show full text]